Wireless communications device including a joint demodulation filter for co-channel interference reduction and related methods

ABSTRACT

A wireless communications device may include a housing and a wireless transmitter and a wireless receiver carried by the housing. The wireless receiver may include a joint demodulation filter for reducing co-channel interference between a desired signal and a co-channel interfering signal which may include an input receiving samples of the desired signal and the co-channel interfering signal, a Viterbi decoder, and a first signal path between the input and the Viterbi decoder comprising a first filter. The joint demodulation filter may further include a second signal path between the input and the Viterbi decoder and comprising a linear finite impulse response (FIR) modeler for generating a channel impulse response estimate for the co-channel interfering signal. Additionally, a third signal path may be between the input and the Viterbi decoder and include a whitened matched filter for generating a channel impulse response estimate for the desired signal.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.60/710,565, filed Aug. 23, 2005, which is hereby incorporated herein inits entirety by reference.

FIELD OF THE INVENTION

The present invention relates to wireless communications systems, suchas cellular communications systems, and, more particularly, to filteringreceived wireless signals to reduce unwanted interference.

BACKGROUND

Cellular communications systems continue to grow in popularity and havebecome an integral part of both personal and business communications.Cellular telephones allow users to place and receive voice calls mostanywhere they travel. However, with ever increasing numbers of cellularphone users comes greater challenges for wireless communications deviceand network providers. One such challenge is addressing interferencecaused between multiple cellular devices operating in a givengeographical area. Cellular devices communicate with a cellular basestation using common or shared wireless communications channels (i.e.,frequencies). Yet, in some cases signals between other devices and abase station using the same channel may cause a desired signal from thebase station to be significantly degraded or even dropped by thehandheld device. Such interference is called co-channel interference.

Because of the increasing load on cellular communicationsinfrastructures, various single-antenna interference cancellation (SAIC)approaches have been investigated to meet requirements for DownlinkAdvanced Receiver Performance (DARP). This effort is being standardizedby the third generation mobile communications system and the ThirdGeneration Partnership Project (3GPP).

One SAIC technique that has been investigated is based upon jointdemodulation of the desired and interfering sequences. Generallyspeaking, this approach begins with a standard least-squares (LS)estimate of the propagation channel and a static channel profile for theinterferer. Then, a modified Viterbi decoder is used in which half ofthe state bits represent the user sequence and the other half representthe interferer. A joint branch metric is minimized and the estimatedsequences for the desired and interfering signal are used in a leastmean squares (LMS) algorithm to update the channel estimates for boththe desired and interfering propagation channel.

The 3GPP initiative has given consideration to the application of jointdemodulation in synchronized wireless networks. See, e.g., “FeasibilityStudy on Single Antenna Interference Cancellation (SAIC) for GSMNetworks,” 3GPP TR 45.903 Version 6.0.1, Release 6, EuropeanTelecommunications Standards Institute, 2004. This is the more limitedcase that requires one to assume that the base station synchronizationdata sequences (i.e., training sequences) of the desired-signal anddominant-interferer overlap, which in turn makes the estimation of theCIRs possible using previously known techniques. It also requires one toassume that the interferer will be dominant for the entire burst.

However, in asynchronous network applications the training sequences ofinterfering signals may not overlap those of the desired signal, whichmakes CIR estimation problematic. Accordingly, further developments maybe desirable to make joint demodulation techniques practical toimplement in both synchronous and asynchronous networks.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of an exemplary Single AntennaInterference Cancellation (SAIC) enabled joint demodulation GlobalSystem for Mobile Communication (GSM) receiver in accordance with thepresent invention.

FIG. 2 is a schematic block diagram of an exemplary embodiment of thejoint demodulation receiver of FIG. 1 shown in greater detail.

FIG. 3 is a graph of simulated performance results for an SAIC jointdemodulation receiver in accordance with the present invention and atypical GMSK receiver in accordance with the prior art.

FIG. 4 is a flow diagram of an exemplary joint demodulation filteringmethod for reducing co-channel interference between a desired signal anda co-channel interfering signal in accordance with the invention.

FIG. 5 is a schematic block diagram of an exemplary wirelesscommunication device in which the joint demodulation receiver of FIG. 1may be used.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present description is made with reference to the accompanyingdrawings, in which preferred embodiments are shown. However, manydifferent embodiments may be used, and thus the description should notbe construed as limited to the embodiments set forth herein. Rather,these embodiments are provided so that this disclosure will be thoroughand complete. Like numbers refer to like elements throughout.

Generally speaking, a wireless communications device including a housingand a wireless transmitter and a wireless receiver carried by thehousing is described herein. In particular, the wireless receiver mayinclude a joint demodulation filter for reducing co-channel interferencebetween a desired signal and a co-channel interfering signal The jointdemodulation filter may include an input receiving samples of thedesired signal and the co-channel interfering signal, a Viterbi decoder,and a first signal path between the input and the Viterbi decodercomprising a first filter The joint demodulation filter may furtherinclude a second signal path between the input and the Viterbi decoderand comprising a linear finite impulse response (FIR) modeler forgenerating a channel impulse response estimate for the co-channelinterfering signal. Additionally, a third signal path may be between theinput and the Viterbi decoder and include a whitened matched filter forgenerating a channel impulse response estimate for the desired signal,

More particularly, the desired signal and the co-channel interferingsignal may each include a training sequence, and the joint demodulationfilter may further include a training-sequence locator upstream of thesecond and third paths and downstream from the input. Additionally, thethird signal path may include a desired-signal channel impulse response(CIR) estimator upstream of the whitened matched filter for generating adesired-signal CIR estimate. Furthermore, the first filter may be afirst finite impulse response (FIR) filter.

The second signal path may include a first summer and a second summerconnected downstream therefrom. Moreover, the second signal path mayfurther include a remodulator between the desired-signal CIR estimatorand the first summer and cooperating therewith for subtracting aremodulated desired-signal training sequence from samples of the desiredsignal and the co-channel interfering signal to thereby generate aninterference signal estimate. In addition, the linear FIR modeler mayinclude a blind interference and CIR estimator, and a second FIR filterdownstream from the blind interference and CIR estimator The Viterbidecoder may also iteratively build a tree of interferer bit sequencehypotheses.

A joint demodulation filtering method for reducing co-channelinterference between a desired signal and a co-channel interferingsignal in a wireless communications receiver may include filteringreceiving samples of the desired signal and the co-channel interferingsignal using a first signal path comprising a first filter. The methodmay further include generating a channel impulse response estimate forthe co-channel interfering signal using a second signal path comprisinga linear finite impulse response (FIR) modeler, and generating a channelimpulse response estimate for the desired signal using a third signalpath comprising a whitened matched filter. In addition, a decodingoperation may be performed based upon the filtered received samples ofthe desired signal and the co-channel interfering signal, the channelimpulse response estimate for the co-channel interfering signal, and thechannel impulse response estimate for the desired signal using a Viterbidecoder.

Turning first to FIGS. 1 and 2, a joint demodulation filter 10 inaccordance with an exemplary embodiment illustratively includes an input11 receiving samples of a desired signal and a co-channel interferingsignal, e.g., from the antenna of a wireless communications device(e.g., a mobile cellular device). That is, the joint demodulation filter10 may advantageously be implemented in a wireless receiver of a mobilewireless communications device. The various components of the jointdemodulation filter 10 may be implemented using software modules and aprocessing circuitry, such as a digital signal processor (DSP), forexample, although other implementations are also possible, as will beappreciated by those skilled in the art. Exemplary components of amobile cellular device in which the joint demodulation filter 10 may beused will be discussed further below with reference to FIG. 5.

The joint demodulation filter 10 further illustratively includes aViterbi decoder 30, and a first signal path 12 between the input 11 andthe Viterbi decoder comprising a first filter 46. In the exemplaryembodiment shown in FIG. 2, the first filter 46 may be a finite infiniteresponse (FIR) filter, such as a matched filter, for example. Also, asecond signal path 13 is included between the input 11 and the Viterbidecoder 30. The second signal path 13 illustratively includes a linearFIR modeler 15 for generating a channel impulse response estimate forthe co-channel interfering signal. Additionally, a third signal path 14is illustratively connected between the input 11 and the Viterbi decoder30. The third signal branch illustratively includes a whitened matchedfilter 44 for generating a channel impulse response estimate for thedesired signal, as will be discussed further below.

Additional components of the exemplary joint demodulation filter 10illustrated in FIG. 2 will now be briefly identified, followed by adescription of the various functions thereof. As noted above, in acellular communications GSM-based network, for example, a desired signaland a co-channel interfering signal will each include a trainingsequence. The joint demodulation filter 10 illustratively includes atraining-sequence locator 20 for the desired signal upstream of thesecond and third paths 13, 14 and downstream from the input 11. Thethird signal path 14 illustratively includes a desired-signal CIRestimator 22 upstream of the whitened matched filter 44 for generating adesired-signal CIR estimate.

The second signal path 13 also illustratively includes a first summer26, a second summer 34 connected downstream from the first summer, and aremodulator 24 between the desired-signal CIR estimator 22 and the firstsummer and cooperating therewith for subtracting a remodulateddesired-signal training sequence from samples of the desired signal andthe co-channel interfering signal to thereby generate an interferencesignal estimate. The linear FIR modeler 15 illustratively includes ablind interference and CIR estimator 28, coupled to the summer 26, and asecond FIR filter 42 downstream from the blind interference and CIRestimator 28, which also receives an input from the whitened matchedfilter 44. The second summer 34 also receives an output of the blindinterference and CIR estimator 28, as shown.

The second signal path 13 further illustratively includes a residualnoise power (Pn) sample offset block 32 between the first and secondsummers 26, 34, a significant interferer component (Pif) sample offsetblock downstream from the second summer, and a Pif/Pn decision block 38downstream from the Pif sample offset block, as will be discussedfurther below. A mixer 40 is downstream from the Pif sample offset block38 and also receives an output of the second FIR filter 42 as shown. Theoutput of the mixer 40 and the output of the whitened matched filter 44are provided to the Viterbi decoder 30, as is the output of the firstFIR 46.

The operation of the joint demodulation receiver 10 will now bedescribed in further detail. As noted above, the joint demodulation (JD)receiver 10 may advantageously be used in wireless communicationssystems, such as in cellular base stations and mobile cellularcommunications devices, for example. Generally speaking, jointdemodulation uses estimates for a channel impulse response (CIR) for adesired signal and a dominant interferer associated therewith. For a GSMimplementation, which will be discussed below, it will be assumed thatthe dominant interferer is a GMSK modulated signal conforming to the GSMspecification.

The joint demodulation approach set forth herein may be applicable toboth synchronized and unsynchronized networks, in that this techniqueuses “blind” interferer data and channel estimation techniques ratherthan making the above-noted assumptions. Once the CIRs have beenestimated, a two-dimensional (joint) adaptive Viterbi state structuremay be used in the equalizer to estimate the data for both the desiredsignal and the interferer.

Simulations of the present joint demodulation technique havedemonstrated greater than 10 dB carrier-to-interference (C/I)improvement at about 0 dB C/I in the raw symbol error rate and frameerror rate for 12.2-rate AMR FS speech. In the simulations, a newjoint-least-squares based technique was used for channel-offsetpositioning and desired and interferer CIR estimation. As noted above,this approach is coupled with blind estimation of the interferer data(i.e., with no a-priori knowledge of the interferer's data).

The present joint demodulation approach may be particularly advantageousin its ability to provide relatively high gains (i.e., in its ability toreceive at very low signal-to-noise ratios (SNRs)) when limited a-prioriknowledge about the interferer is available, as will be discussedfurther below. Yet, the Viterbi algorithm (VA) complexity may alsoincrease, (depending on the number of states used to model theinterferer), thus the processing requirements and the additionalcomplexity of the channel/data estimators may be a factor in somesoftware or hardware implementations.

For the test configuration, a system level Block Error Rate (BLER)simulator was extended to support all of the interferer models/scenariosbeing used by the 3GPP DARP work group. This extension also allows newinterferer models to be developed as needed. The simulations wereperformed using Matlab.

The joint demodulation approach assumes that the dominant interferencecomponent may be modeled as the noisy output of afinite-impulse-response (FIR) (unknown) filter with unknown, binary,random input (interferer) data. In the case of a dominant GMSK-modulatedinterferer, this assumption holds even if there are additional, weakerinterference signals present, which are treated as residual noise.Moreover, this approach may be applied to other interferer modulationtypes using the above modeling assumption.

Referring again to FIG. 2, the steps associated with the jointdemodulation approach are as follows. First, a base station trainingsequence (TS) for the desired signal is found (Block 20), the CIR forthe desired signal is estimated (Block 22), and the re-modulated desiredtraining sequence is removed from the input samples to form theinterferer-signal estimate (Block 24). Furthermore, the “blind”estimation of the interferer CIR and data is performed based upon theinterferer-signal estimate, at Blocks 26, 28. Next, a jointleast-squares desired/interferer channel estimation using the desiredtraining sequence and estimated interferer data is performed at Block30, as will be discussed further below.

In addition, the foregoing steps may be repeated (or performed in avectorized form) at multiple input sample offsets (as the timing offsetvaries). As such, the offset yielding the minimal residual noise power(Pn) may be selected, and a determination may be made as to whether themodel applies (i.e., was a significant interferer component (Pif)detected or not), at Blocks 32, 34, 36, and 38. If so, demodulation isperformed using a joint-demodulation (multi-dimensional state) Viterbialgorithm that estimates and removes the interference jointly with theestimation of the desired-signal data (Block 30).

Initially, the desired channel impulse response was estimated using aconventional training-sequence correlation (i.e., “channel-sounding”)method, as will be appreciated by those skilled in the art. At low C/Ilevels, the least-squares method provides the initial desired channelimpulse response estimate by multiplying the input samples by a constant(pre-computed) matrix (A^(H)A)⁻¹A^(H), where A is the training-sequenceconvolution matrix of the desired signal.

For estimating the interferer, the above-noted SAIC Feasibility Studyassumes a synchronous network model. More particularly, this modelassumes that the training sequence of the interfering signal is alignedwith the desired signal's training sequence within a −1 to +4 symboloffset. In this case, the interferer channel impulse response can beestimated using the training-sequence correlation technique (or leastsquares, since the training-sequence data is known) after removing thedesired signal's (re-modulated) training sequence from the receivedsamples.

However, to widen the potential applicability of the joint-demodulationapproach to the asynchronous network case where the interferer dataduring the desired signal's training sequence is unknown, blind channeland data estimation and demodulation techniques are used. By way ofbackground in this regard, reference is made to the article by Seshadrientitled “Joint Data and Channel Estimation Using Blind Trellis SearchTechniques,” IEEE Trans. on Communications, vol. 42, no. 2/3/4, pgs.1000-1011, and the article by Daneshgaran et al. entitled “BlindEstimation of Output Labels of SIMO Channels Based on a Novel ClusteringAlgorithm,” IEEE Communications Letters, vol. 2, no. 11, November 1998,pgs. 307-309, both of which are hereby incorporated herein in theirentireties by reference.

One particular difficulty of performing blind interferer estimation isthe very small number of “observable” interferer (i.e., noisy) samplesduring the desired signal's training-sequence window. By way ofreference, the sequence window is the length of the desired trainingsequence (for this embodiment, the training sequence length is 26, asdefined by the GSM 05-series standards) less the desired signal's CIRlength (5 is chosen by this simulation, however other values between 1and 7 are possible depending on the channel models as defined by the GSMstandards) plus one, or: 26−5+1=22 (twenty-two) in the present example.

This approach uses an algorithm which combines concepts of vectorquantization and sequential decoding of convolutional codes. Thealgorithm is based on two assumptions: (1) the interferer signal may bemodeled with a linear Finite Impulse Response (FIR) source (Block 28);and (2) the interferer signal is corrupted by residual additive white(i.e., uncorrelated) Gaussian noise (after removing the estimateddesired signal) (FIG. 1, 26).

With these two assumptions, the algorithm iteratively builds a tree ofinterferer bit sequence hypotheses. For each new bit added to a bitsequence hypothesis, it computes the new FIR state (or codebook index,as will be apparent to those skilled in the art of vector quantization)and averages all input samples corresponding to the same state in aparticular sequence to estimate the FIR output (codebook value) for thatstate. The distortion of a bit sequence is what remains after removingthe sequence's FIR outputs from the input samples (FIG. 2, 36). Afterkeeping up to W (search width parameter) bit sequences with the lowestdistortions, each sequence is extended by another 0/1 bit to yield twonew sequences (2 W total), and the process of re-estimating FIR outputsof each sequence is repeated followed by keeping the W sequences withminimum distortion (e.g., one-half the sequences). When the sequencelength reaches the number of interferer-signal samples available (22 forthis embodiment, as described above), the sequence with the lowestdistortion out of W candidates is chosen.

This above-described algorithm provides the initial interferer data andchannel impulse response estimates for subsequent joint least-squaresdesired-signal and interferer-channel estimation. At C/I levels below 5dB, the CIR position (offset), and CIR value estimation for the desiredand interferer is affected by the cross-correlation of the desired andinterferer data sequences. However, using the previously obtainedinterferer data estimate, a joint least-squares channel estimation ispossible that removes (i.e., accounts for) this cross-correlation asfollows:

${{{\begin{matrix}A^{\prime} \\B^{\prime}\end{matrix}}s} = {{\begin{matrix}{A^{\prime}A} & {B^{\prime}A} \\{A^{\prime}B} & {B^{\prime}B}\end{matrix}}{\begin{matrix}h \\g\end{matrix}}}},$where s contains the input samples during the desired training-sequencewindow (26−5+1=22 as described previously), A (N×Lh) and B (N×Lg) arethe desired-signal and interferer data-sequence convolution matrices (Ais known and constant, B is an estimate for the interferer), and h and gare the desired-signal and interferer CIRs respectively that result fromsolving the above equations with Lh (5 in this embodiment) the length ofh, and Lg (3 chosen for this embodiment) the length of g.

Once estimates of the desired and interferer channel impulse responsesare available, a two-dimensional state Viterbi algorithm may be appliedFor a Euclidean distance metric, the whitened discrete time model filter(WMF) is computed from the estimated desired CIR (Block 44). Thecomputation is also applied to the interferer CIR, and the three (Lg)largest resulting taps are used to form the interferer codebook (i.e., aset of possible interferer channel FIR outputs) Of course, other numbersof taps Lh and Lg may also be used in some embodiments

The resulting desired-signal and interferer codebooks are passed to thejoint-demodulation Viterbi algorithm The returned soft-decision metricsinclude the forward and backward recursion using the difference of theodd/even state minimum metrics at each stage (not path) as the softdecision value and sign.

Turning now to FIG. 3, simulated results for TCH-AFS 12.2 rate speechfor a typical urban fading profile at 50 km vehicle speeds (TU-50) atthe 1950 MHz band without the use of frequency hopping and usinginterferer model DTS1 are shown, as will be appreciated by those skilledin the art. C/I is the average carrier-to-interference ratio.

The dotted lines 50 and 51 represent the SER (symbol error rate) and FER(frame error rates) of the conventional GMSK receiver. The dashed lines53 and 54 represent the performance of the above-described SAIC-JDreceiver. The solid lines 55 and 56 represent the performance of ahigher-complexity SAIC-JD receiver in accordance with an exemplaryembodiment of the invention in which the blind vector quantization ofthe interferer is performed using recursive least squares (RLS) updateswhile the interferer symbol sequence hypotheses are formed andevaluated. As will be appreciated by those skilled in the art, theperformance plot demonstrates that both of the SAIC-JD receivers providesignificant improvement over the conventional receiver in a highinterference environment.

The amount of residual “noise” power remaining in the desired signal'straining-sequence window after removing the desired (i.e., estimated)samples may be used as a test of model “fit” in some embodiments. Ifremoving the subsequently estimated interferer does not reduce theresidual power significantly, a non-interference signal model may beselected, and vice-versa.

A joint demodulation filtering method for reducing co-channelinterference between a desired signal and a co-channel interferingsignal will now be described with reference to FIG. 4. Beginning atBlock 60, receiving samples of the desired signal and the co-channelinterfering signal are filtered using a first signal path 12 comprisinga first filter 46, at Block 61. The method may further includegenerating a channel impulse response estimate for the co-channelinterfering signal using a second signal path 13 comprising a linearfinite impulse response (FIR) modeler 15, at Block 62, and generating achannel impulse response estimate for the desired signal using a thirdsignal path 14 comprising a whitened matched filter 44, at Block 63 Inaddition, a decoding operation may be performed based upon the filteredreceived samples of the desired signal and the co-channel interferingsignal, the channel impulse response estimate for the co-channelinterfering signal, and the channel impulse response estimate for thedesired signal using a Viterbi decoder 30, at Block 64, thus concludingthe illustrated method (Block 65).

One example of a hand-held mobile wireless communications device 1000that may be used in accordance with the system 20 is further describedin the example below with reference to FIG. 5. The device 1000illustratively includes a housing 1200, a keypad 1400 and an outputdevice 1600. The output device shown is a display 1600, which ispreferably a full graphic LCD. Other types of output devices mayalternatively be utilized. A processing device 1800 is contained withinthe housing 1200 and is coupled between the keypad 1400 and the display1600. The processing device 1800 controls the operation of the display1600, as well as the overall operation of the mobile device 1000, inresponse to actuation of keys on the keypad 1400 by the user.

The housing 1200 may be elongated vertically, or may take on other sizesand shapes (including clamshell housing structures). The keypad mayinclude a mode selection key, or other hardware or software forswitching between text entry and telephony entry.

In addition to the processing device 1800, other parts of the mobiledevice 1000 are shown schematically in FIG. 5. These include acommunications subsystem 1001; a short-range communications subsystem1020; the keypad 1400 and the display 1600, along with otherinput/output devices 1060, 1080, 1100 and 1120; as well as memorydevices 1160, 1180 and various other device subsystems 1201. The mobiledevice 1000 is preferably a two-way RF communications device havingvoice and data communications capabilities. In addition, the mobiledevice 1000 preferably has the capability to communicate with othercomputer systems via the Internet.

Operating system software executed by the processing device 1800 ispreferably stored in a persistent store, such as the flash memory 1160,but may be stored in other types of memory devices, such as a read onlymemory (ROM) or similar storage element In addition, system software,specific device applications, or parts thereof, may be temporarilyloaded into a volatile store, such as the random access memory (RAM)1180. Communications signals received by the mobile device may also bestored in the RAM 1180.

The processing device 1800, in addition to its operating systemfunctions, enables execution of software applications 1300A-1300N on thedevice 1000. A predetermined set of applications that control basicdevice operations, such as data and voice communications 1300A and1300B, may be installed on the device 1000 during manufacture. Inaddition, a personal information manager (PIM) application may beinstalled during manufacture. The PIM is preferably capable oforganizing and managing data items, such as e-mail, calendar events,voice mails, appointments, and task items. The PIM application is alsopreferably capable of sending and receiving data items via a wirelessnetwork 1401. Preferably, the PIM data items are seamlessly integrated,synchronized and updated via the wireless network 1401 with the deviceuser's corresponding data items stored or associated with a hostcomputer system.

Communication functions, including data and voice communications, areperformed through the communications subsystem 1001, and possiblythrough the short-range communications subsystem. The communicationssubsystem 1001 includes a receiver 1500, a transmitter 1520, and one ormore antennas 1540 and 1560. In addition, the communications subsystem1001 also includes a processing module, such as a digital signalprocessor (DSP) 1580, and local oscillators (LOs) 1601. The specificdesign and implementation of the communications subsystem 1001 isdependent upon the communications network in which the mobile device1000 is intended to operate. For example, a mobile device 1000 mayinclude a communications subsystem 1001 designed to operate with theMobitex™, Data TAC™ or General Packet Radio Service (GPRS) mobile datacommunications networks, and also designed to operate with any of avariety of voice communications networks, such as AMPS, TDMA, CDMA,WCDMA, PCS, GSM, EDGE, etc. Other types of data and voice networks, bothseparate and integrated, may also be utilized with the mobile device1000. The mobile device 1000 may also be compliant with othercommunications standards such as 3GSM, 3GPP, UMTS, etc.

Network access requirements vary depending upon the type ofcommunication system. For example, in the Mobitex and DataTAC networks,mobile devices are registered on the network using a unique personalidentification number or PIN associated with each device. In GPRSnetworks, however, network access is associated with a subscriber oruser of a device. A GPRS device therefore requires a subscriber identitymodule, commonly referred to as a SIM card, in order to operate on aGPRS network.

When required network registration or activation procedures have beencompleted, the mobile device 1000 may send and receive communicationssignals over the communication network 1401. Signals received from thecommunications network 1401 by the antenna 1540 are routed to thereceiver 1500, which provides for signal amplification, frequency downconversion, filtering, channel selection, etc., and may also provideanalog to digital conversion. Analog-to-digital conversion of thereceived signal allows the DSP 1580 to perform more complexcommunications functions, such as demodulation and decoding. In asimilar manner, signals to be transmitted to the network 1401 areprocessed (e.g. modulated and encoded) by the DSP 1580 and are thenprovided to the transmitter 1520 for digital to analog conversion,frequency up conversion, filtering, amplification and transmission tothe communication network 1401 (or networks) via the antenna 1560.

In addition to processing communications signals, the DSP 1580 providesfor control of the receiver 1500 and the transmitter 1520. For example,gains applied to communications signals in the receiver 1500 andtransmitter 1520 may be adaptively controlled through automatic gaincontrol algorithms implemented in the DSP 1580.

In a data communications mode, a received signal, such as a text messageor web page download, is processed by the communications subsystem 1001and is input to the processing device 1800. The received signal is thenfurther processed by the processing device 1800 for an output to thedisplay 1600, or alternatively to some other auxiliary I/O device 1060.A device user may also compose data items, such as e-mail messages,using the keypad 1400 and/or some other auxiliary I/O device 1060, suchas a touchpad, a rocker switch, a thumb-wheel, or some other type ofinput device The composed data items may then be transmitted over thecommunications network 1401 via the communications subsystem 1001.

In a voice communications mode, overall operation of the device issubstantially similar to the data communications mode, except thatreceived signals are output to a speaker 1100, and signals fortransmission are generated by a microphone 1120. Alternative voice oraudio I/O subsystems, such as a voice message recording subsystem, mayalso be implemented on the device 1000. In addition, the display 1600may also be utilized in voice communications mode, for example todisplay the identity of a calling party, the duration of a voice call,or other voice call related information.

The short-range communications subsystem enables communication betweenthe mobile device 1000 and other proximate systems or devices, whichneed not necessarily be similar devices. For example, the short-rangecommunications subsystem may include an infrared device and associatedcircuits and components, or a Bluetooth™ communications module toprovide for communication with similarly-enabled systems and devices.

Many modifications and other embodiments will come to the mind of oneskilled in the art having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it isunderstood that various modifications and embodiments are intended to beincluded within the scope of the appended claims.

1. A wireless communications device comprising: a housing; and awireless transmitter and a wireless receiver carried by said housing;said wireless receiver comprising a joint demodulation filter forreducing co-channel interference between a desired signal and aco-channel interfering signal, the filter comprising an input receivingsamples of the desired signal and the co-channel interfering signal, aViterbi decoder, a first signal path between said input and said Viterbidecoder comprising a first filter, a second signal path between saidinput and said Viterbi decoder and comprising a linear finite impulseresponse (FIR) modeler for generating a channel impulse responseestimate for the co-channel interfering signal, and a third signal pathbetween said input and said Viterbi decoder and comprising a whitenedmatched filter for generating a channel impulse response estimate forthe desired signal.
 2. The wireless communications device of claim 1wherein the desired signal and the co-channel interfering signal eachincludes a training sequence; and wherein said joint demodulation filterfurther comprises a training-sequence locator upstream of said secondand third paths and downstream from said input.
 3. The wirelesscommunications device of claim 1 wherein said third signal pathcomprises a desired-signal channel impulse response (CIR) estimatorupstream of said whitened matched filter for generating a desired-signalCIR estimate.
 4. The wireless communications device of claim 1 whereinsaid first filter comprises a first finite impulse response (FIR)filter.
 5. The wireless communications device of claim 3 wherein saidsecond signal path comprises a first summer and a second summerconnected downstream therefrom.
 6. The wireless communications device ofclaim 5 wherein said second signal path further comprises a remodulatorbetween said desired-signal CIR estimator and said first summer andcooperating therewith for subtracting a remodulated desired-signaltraining sequence from samples of the desired signal and the co-channelinterfering signal to thereby generate an interference signal estimate.7. The wireless communications device of claim 1 wherein said linear FIRmodeler comprises a blind interference and CIR estimator, and a secondFIR filter downstream from said blind interference and CIR estimator. 8.The wireless communications device of claim 1 wherein said wirelessreceiver comprises a cellular receiver.
 9. A wireless communicationsdevice comprising: a housing; and a wireless transmitter and a wirelessreceiver carried by said housing; said wireless receiver comprising ajoint demodulation filter for reducing co-channel interference between adesired signal and a co-channel interfering signal where the desiredsignal and the co-channel interfering signal each includes a trainingsequence, the filter comprising an input receiving samples of thedesired signal and the co-channel interfering signal, a Viterbi decoder,a first signal path between said input and said Viterbi decodercomprising a first finite impulse response (FIR) filter, a second signalpath between said input and said Viterbi decoder and comprising a linearfinite impulse response (FIR) modeler for generating a channel impulseresponse estimate for the co-channel interfering signal, a third signalpath between said input and said Viterbi decoder and comprising awhitened matched filter for generating a channel impulse responseestimate for the desired signal, and a training-sequence locatorupstream of said second and third paths and downstream from said input.10. The wireless communications device of claim 9 wherein said thirdsignal path comprises a desired-signal channel impulse response (CIR)estimator upstream of said whitened matched filter for generating adesired-signal CIR estimate.
 11. The wireless communications device ofclaim 10 wherein said second signal path comprises a first summer and asecond summer connected downstream therefrom.
 12. The wirelesscommunications device of claim 11 wherein said second signal pathfurther comprises a remodulator between said desired-signal CIRestimator and said first summer and cooperating therewith forsubtracting a remodulated desired-signal training sequence from samplesof the desired signal and the co-channel interfering signal to therebygenerate an interference signal estimate.
 13. The wirelesscommunications device of claim 9 wherein said linear FIR modelercomprises a blind interference and CIR estimator, and a second FIRfilter downstream from said blind interference and CIR estimator.
 14. Amethod for reducing co-channel interference between a desired signal anda co-channel interfering signal in a receiver of a wirelesscommunications device, the method comprising: filtering received samplesof the desired signal and the co-channel interfering signal using afirst signal path comprising a first filter; generating a channelimpulse response estimate for the co-channel interfering signal using asecond signal path comprising a linear finite impulse response (FIR)modeler; generating a channel impulse response estimate for the desiredsignal using a third signal path comprising a whitened matched filter;and performing a decoding operation based upon the filtered receivedsamples of the desired signal and the co-channel interfering signal, thechannel impulse response estimate for the co-channel interfering signal,and the channel impulse response estimate for the desired signal using aViterbi decoder.
 15. The method of claim 14 wherein the desired signaland the co-channel interfering signal each includes a training sequence;and further comprising performing a training-sequence location upstreamof the second and third paths and downstream from the input.
 16. Themethod of claim 14 wherein the third signal path comprises adesired-signal channel impulse response (CIR) estimator upstream of thewhitened matched filter for generating a desired-signal CIR estimate.17. The method of claim 14 wherein the first filter comprises a firstfinite impulse response (FIR) filter.
 18. The method of claim 16 whereinthe second signal path comprises a first summer and a second summerconnected downstream therefrom.
 19. The method of claim 18 wherein thesecond signal path further comprises a remodulator between thedesired-signal CIR estimator and the first summer and cooperatingtherewith for subtracting a remodulated desired-signal training sequencefrom samples of the desired signal and the co-channel interfering signalto thereby generate an interference signal estimate.
 20. The method ofclaim 14 wherein the linear FIR modeler comprises a blind interferenceand CIR estimator, and a second FIR filter downstream from the blindinterference and CIR estimator.